A Comparative Study of Cluster-Head Selection Algorithms in VANET

Thakur, Poonam; Ganpati, Anita

doi:10.1007/978-981-15-3020-3_13

Cited by 5 publications

(4 citation statements)

References 28 publications

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“…Typically, vehicles in close proximity are grouped together in a cluster based on various key parameters and metrics. The vehicles present in the cluster are known as [9]:…”

Section: Clustering In Vanetmentioning

confidence: 99%

Clustering Review in Vehicular Ad hoc Networks: Algorithms, Comparisons, Challenges and Solutions

Jabbar¹,

Trabelsi

2022

Int. J. Interact. Mob. Technol.

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The Vehicular Ad-hoc Network (VANET) represents a new future for dynamic information dissemination between societies. VANET has a wide range of applications in a variety of aspects, including Intelligent Transportation Systems (ITS). VANET has some characteristics, like highly dynamic topology and intermittent connections. These characteristics lead to untrustworthy information transmission in VANET. Vehicle clustering is an efficient approach to improve the scalability of the network and connection reliability. The performance of the clustering is also affected by VANET characteristics. This article provides a comprehensive description of VANET clustering algorithms. The most notable clustering algorithms introduced between 1999 and 2021 are reviewed. A complete survey on clustering in VANETs is provided based upon the clustering process. The clustering process in most algorithms is explored in the aspects of CH selection metrics, cluster formation, and cluster maintenance. The clustering techniques based on some parameters like stability, convergence, overhead, and latency are compared. Some of the most common problems, as well as the approaches employed to solve them, are also discussed. Also, the performance parameters which evaluate the clustering approaches are summarized.

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“…Typically, vehicles in close proximity are grouped together in a cluster based on various key parameters and metrics. The vehicles present in the cluster are known as [9]:…”

Section: Clustering In Vanetmentioning

confidence: 99%

Clustering Review in Vehicular Ad hoc Networks: Algorithms, Comparisons, Challenges and Solutions

Jabbar¹,

Trabelsi

2022

Int. J. Interact. Mob. Technol.

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“…(7) Each vehicle's position is estimated through the Kalman filter that intimates the RSU about the range of vehicles and ensures noninterruptible communication with smooth transitions among the RSU. (8) e network over time evolves into several dynamic clusters; the maximum hops between the cluster head and its CM are maxing hop. (9) Packet data of size 64 bits is taken.…”

Section: Network Modelmentioning

confidence: 99%

“…ere are several benefits like efficient bandwidth, proper distribution of resources, and scalability that the clustering approach offers [7]. ere are many methods employed for clustering in VANETs [8,9]. e urban roads are complicated as vehicle positions change frequently; they have unevenly distributed; thus, their routing and the forwarding capabilities change with their position.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Dynamic Stable Cluster Head Selection in VANET

Saleem

Zhou

Sarwar

et al. 2021

Journal of Advanced Transportation

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VANET is the spontaneous evolving creation of a wireless network, and clustering in these networks is a challenging task due to rapidly changing topology and frequent disconnection in networks. The cluster head (CH) stability plays a prominent role in robustness and scalability in the network. The stable CH ensures minimum intra- and intercluster communication, thereby reducing the overhead. These challenges lead the authors to search for a CH selection method based on a weighted amalgamation of four metrics: befit factor, community neighborhood, eccentricity, and trust. The stability of CH depends on the vehicle’s speed, distance, velocity, and change in acceleration. These all are included in the befit factor. Also, the accurate location of the vehicle in changing the model is very vital. Thus, the predicted location with the Kalman filter’s help is used to evaluate CH stability. The results have shown better performance than the existing state of the art for the befit factor. The change in dynamics and frequent disconnection in communication links due to the vehicle’s high speed are inevitable. To comprehend this problem, a graphing approach is used to evaluate the eccentricity and the community neighborhood. The link reliability is calculated using the eigengap heuristic. The last metric is trust; this is one of the concepts that has not been included in the weighted approach to date as per the literature. An adaptive spectrum sensing is designed for evaluating the trust values specifically for the primary users. A deep recurrent learning network, commonly known as long short-term memory (LSTM), is trained for the probability of detection with various signals and noise conditions. The false rate has drastically reduced with the usage of LSTM. The proposed scheme is tested on the real map of Chengdu, southwestern China’s Sichuan province, with different vehicular mobilities. The comparative study with the individual and weighted metric has shown significant improvement in the cluster head stability during high vehicular density. Also, there is a considerable increase in network performance in energy, packet delay, packet delay ratio, and throughput.

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“…Some researchers have involved a wider range of vehicle attributes for CH selection than the typical road environment information. For example, VANETs clustering was among the approaches in the perspective of commercializing the CH selection and that was by including the price offer of CH as an additional variable to the mobility and topology information [34]. An example is a work of [35] where fuzzybased logic was used for clustering control and game theory was used for CH competition.…”

Section: Literature Surveymentioning

confidence: 99%

A Center-Based Stable Evolving Clustering Algorithm With Grid Partitioning and Extended Mobility Features for VANETs

Talib

Hassan

Alamery³

et al. 2020

IEEE Access

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VANETs clustering is an emerging research topic that serves in the intelligent transportation systems of today's technology. It aims at segmenting the moving vehicles in the road environment into subgroups named clusters, with cluster heads for enabling effective and stable routing. Most of the VANETs clustering approaches are based on distributed models which make the decision of clusters creation lacking the global view of the vehicle's distribution and mobility in the environment. However, the availability of the LTE and long ranges of base station motivated researchers recently to provide center-based approaches. Unlike existing center-based clustering approaches of VANETs, this article uses the road segmenting phase named grid partitioning before providing summarized information to the clustering center. Furthermore, it presents an integrated approach as a combination of all the clustering tasks including assigning, cluster head selection, removing, and merging. Evaluation of the proposed approach named center-based evolving clustering based on grid partitioning (CEC-GP) is proven superior from the perspective of efficiency, stability, and consistency. An improvement percentage of the efficiency in (CEC-GP) over the benchmarks Center based stable clustering (CBSC) and evolving data clustering algorithm (EDCA) is 65% and 394% respectively. INDEX TERMS vehicular ad hoc networks, VANETs clustering, Center-based clustering, evolving clustering, grid based-clustering.

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A Comparative Study of Cluster-Head Selection Algorithms in VANET

Cited by 5 publications

References 28 publications

Clustering Review in Vehicular Ad hoc Networks: Algorithms, Comparisons, Challenges and Solutions

Clustering Review in Vehicular Ad hoc Networks: Algorithms, Comparisons, Challenges and Solutions

Deep Learning-Based Dynamic Stable Cluster Head Selection in VANET

A Center-Based Stable Evolving Clustering Algorithm With Grid Partitioning and Extended Mobility Features for VANETs

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